Search · four archives

Results for machine learning code

or ask it as a questionAsk the stacks →

ArchivesarXiv PubMed bioRxiv chemRxiv

WhenAny time Past month Past year Past 5 years

OrderMost relevant Most cited Newest

11 papers · ranked by Valyu relevance

01
PPubMed23 Feb 2023
Code4ML: a large-scale dataset of annotated Machine Learning code
Anastasia Drozdova, Ekaterina Trofimova, Polina Guseva, Anna Scherbakova + 2 more
The use of program code as a data source is increasingly expanding among data scientists. The purpose of the usage varies from the semantic classification of code to the automatic generation of programs. However, the machine learning model application is somewhat limited without annotating the code snippets. To address…
88relevance
02
PPubMed27 Nov 2023
Machine learning code snippets semantic classification
Valeriy Berezovskiy, Anastasia Gorodilova, Ekaterina Trofimova, Andrey Ustyuzhanin + 1 more
'Andrey Ustyuzhanin' 'Syed Hassan Shah'] Program code has recently become a valuable active data source for training various data science models, from code classification to controlled code synthesis. Annotating code snippets play an essential role in such tasks. This article presents a novel approach that leverages…
84relevance
03
BbioRxiv12 May 2017
Easyml: Easily Build and Evaluate Machine Learning Models
Paul Hendricks, Woo-Young Ahn
The easyml (easy machine learning) package lowers the barrier to entry to machine learning and is ideal for undergraduate/graduate students, and practitioners who want to quickly apply machine learning algorithms to their research without having to worry about the best practices of implementing each algorithm. The…
78relevance
04
BbioRxiv20 Dec 2023
Using a decision tree to predict the number of COVID cases: a tutorial for beginners
Lucy Moctezuma, Lorena Benitez Rivera, Florentine van Nouhuijs, Faye Orcales + 4 more
This manuscript describes the development of a module that is part of a learning platform named “NIGMS Sandbox for Cloud-based Learning” https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers…
76relevance
05
PPubMed29 May 2023
Python code smells detection using conventional machine learning models
Rana Sandouka, Hamoud Aljamaan, Stephen Piccolo
Code smells are poor code design or implementation that affect the code maintenance process and reduce the software quality. Therefore, code smell detection is important in software building. Recent studies utilized machine learning algorithms for code smell detection. However, most of these studies focused on code…
74relevance
06
PPubMed17 Mar 2022
On the adequacy of static analysis warnings with respect to code smell prediction
Fabiano Pecorelli, Savanna Lujan, Valentina Lenarduzzi, Fabio Palomba + 1 more
Code smells are poor implementation choices that developers apply while evolving source code and that affect program maintainability. Multiple automated code smell detectors have been proposed: while most of them relied on heuristics applied over software metrics, a recent trend concerns the definition of machine…
72relevance
07
BbioRxiv7 Jun 2026
A Web-based software toolkit for accessible and best-practice machine learning analyses in biomedical research
Paulo Lyra, Junhao Qiu, Khai Dang, Alyssa Pybus + 6 more
Machine learning is increasingly central to biomedical research, but using machine learning well often requires substantial computational expertise and methodological care to produce high-quality results. To make machine learning tools more accessible to biomedical researchers while supporting best-practice approaches…
71relevance
08
BbioRxiv30 Jan 2026
Agentomics: An Agentic System that Autonomously Develops Novel State-of-the-art Solutions for Biomedical Machine Learning Tasks
Vlastimil Martinek, Andrea Gariboldi, Dimosthenis Tzimotoudis, Mark Galea + 7 more
Extracting knowledge from biomedical data is crucial for advancing our understanding of biological systems and developing novel therapeutics. The quantity, quality, and resolution of biomedical data constantly evolves, requiring the automation of biomedical machine learning (ML). Existing Automated ML tools lack…
71relevance
09
BbioRxiv17 Jul 2025
Evaluating large language models in biomedical data science challenges through a classroom experiment
Huifang Ma, Zhicheng Ji
Large language models have shown remarkable capabilities in algorithm design, but their effectiveness in solving data science challenges remains poorly understood. We conducted a classroom experiment in which graduate students used large language models (LLMs) to solve biomedical data science challenges on Kaggle.…
70relevance
10
PPubMed15 Aug 2024
Dynamic stacking ensemble for cross-language code smell detection
Hamoud Aljamaan, Muhammad Aleem
Code smells refer to poor design and implementation choices by software engineers that might affect the overall software quality. Code smells detection using machine learning models has become a popular area to build effective models that are capable of detecting different code smells in multiple programming languages.…
69relevance
11
BbioRxiv14 Jun 2023
SnakeFace: a transfer learning based app for snake classification
Jorge Guerra Pires
Introduction: deep learning emerged in 2012 as one of the most important machine learning technologies, reducing image identification error from 25% to 5%. This article has two goals: 1) to demonstrate to the general public the ease of building state-of-the-art machine learning models without coding expertise; 2) to…
68relevance

Results for machine learning code

Code4ML: a large-scale dataset of annotated Machine Learning code

Machine learning code snippets semantic classification

Easyml: Easily Build and Evaluate Machine Learning Models

Using a decision tree to predict the number of COVID cases: a tutorial for beginners

Python code smells detection using conventional machine learning models

On the adequacy of static analysis warnings with respect to code smell prediction

A Web-based software toolkit for accessible and best-practice machine learning analyses in biomedical research

Agentomics: An Agentic System that Autonomously Develops Novel State-of-the-art Solutions for Biomedical Machine Learning Tasks

Evaluating large language models in biomedical data science challenges through a classroom experiment

Dynamic stacking ensemble for cross-language code smell detection

SnakeFace: a transfer learning based app for snake classification

Code4ML: a large-scale dataset of annotated Machine Learning code

Machine learning code snippets semantic classification

Easyml: Easily Build and Evaluate Machine Learning Models

Using a decision tree to predict the number of COVID cases: a tutorial for beginners

Python code smells detection using conventional machine learning models

On the adequacy of static analysis warnings with respect to code smell prediction

A Web-based software toolkit for accessible and best-practice machine learning analyses in biomedical research

Agentomics: An Agentic System that Autonomously Develops Novel State-of-the-art Solutions for Biomedical Machine Learning Tasks

Evaluating large language models in biomedical data science challenges through a classroom experiment

Dynamic stacking ensemble for cross-language code smell detection

SnakeFace: a transfer learning based app for snake classification